mirror of
https://github.com/MLSysBook/TinyTorch.git
synced 2026-04-29 16:12:34 -05:00
b58c82df1f
Replaced placeholder numbers with actual measurements from benchmarking script. Numbers show TinyTorch's pure Python implementations are 100-10,000× slower than PyTorch, demonstrating the pedagogical value of experiencing performance reality. Real benchmark results: - MatMul (1K×1K): 1.0s vs 0.9ms = 1,090× slower - Conv2d (CIFAR batch): 97s vs 10ms = 10,017× slower - Softmax (10K elem): 6ms vs 0.05ms = 134× slower Methodology: - MatMul: Double-loop with numpy dot for inner loop - Conv2d: Pure 7-nested-loop implementation as shown in paper - Softmax: Pure Python loops for max, exp, sum, normalize Created benchmark_quick.py script that measures actual performance using implementations that match what students write in the curriculum. Conv2d uses single-image timing extrapolated to full batch for speed. Updated paper text to reference actual measured values (97s vs 10ms) instead of placeholders, strengthening the experiencing performance reality pedagogical argument. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
282 lines
8.7 KiB
Python
282 lines
8.7 KiB
Python
#!/usr/bin/env python3
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"""
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Benchmark script to generate real performance numbers for Table 3 in the paper.
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Compares TinyTorch implementations against PyTorch on CPU.
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"""
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import time
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import numpy as np
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import torch
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import sys
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from pathlib import Path
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# Add TinyTorch to path
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repo_root = Path(__file__).parent.parent
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sys.path.insert(0, str(repo_root))
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# Import TinyTorch components
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try:
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from tinytorch.core import Tensor as TTTensor
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from tinytorch.nn import Conv2d as TTConv2d
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TINYTORCH_AVAILABLE = True
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except ImportError:
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print("Warning: TinyTorch not available. Will create mock implementations.")
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TINYTORCH_AVAILABLE = False
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def benchmark_function(func, *args, warmup=3, runs=10):
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"""Benchmark a function with warmup and multiple runs."""
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# Warmup
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for _ in range(warmup):
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func(*args)
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# Actual timing
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times = []
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for _ in range(runs):
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start = time.perf_counter()
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func(*args)
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end = time.perf_counter()
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times.append(end - start)
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return np.mean(times), np.std(times)
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def benchmark_matmul():
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"""Benchmark matrix multiplication: 1000x1000 @ 1000x1000"""
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print("\n=== Benchmarking Matrix Multiplication (1K×1K) ===")
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# PyTorch
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pt_a = torch.randn(1000, 1000)
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pt_b = torch.randn(1000, 1000)
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def pt_matmul():
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return torch.mm(pt_a, pt_b)
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pt_mean, pt_std = benchmark_function(pt_matmul)
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print(f"PyTorch: {pt_mean*1000:.2f} ms ± {pt_std*1000:.2f} ms")
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# TinyTorch
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if TINYTORCH_AVAILABLE:
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# Use TinyTorch's actual implementation
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tt_a = TTTensor(pt_a.numpy())
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tt_b = TTTensor(pt_b.numpy())
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def tt_matmul():
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return tt_a @ tt_b
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tt_mean, tt_std = benchmark_function(tt_matmul, warmup=1, runs=5)
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print(f"TinyTorch: {tt_mean*1000:.2f} ms ± {tt_std*1000:.2f} ms")
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else:
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# Pure Python naive implementation
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a = pt_a.numpy()
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b = pt_b.numpy()
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def naive_matmul():
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n, m, p = a.shape[0], a.shape[1], b.shape[1]
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result = np.zeros((n, p))
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for i in range(n):
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for j in range(p):
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for k in range(m):
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result[i, j] += a[i, k] * b[k, j]
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return result
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tt_mean, tt_std = benchmark_function(naive_matmul, warmup=1, runs=3)
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print(f"TinyTorch (naive): {tt_mean*1000:.2f} ms ± {tt_std*1000:.2f} ms")
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ratio = tt_mean / pt_mean
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print(f"Ratio: {ratio:.0f}×")
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return pt_mean * 1000, tt_mean * 1000, ratio
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def benchmark_conv2d():
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"""Benchmark Conv2d on CIFAR batch: (128, 3, 32, 32) through 32 filters 5×5"""
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print("\n=== Benchmarking Conv2d (CIFAR batch) ===")
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batch_size = 128
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in_channels = 3
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out_channels = 32
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kernel_size = 5
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# PyTorch
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pt_input = torch.randn(batch_size, in_channels, 32, 32)
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pt_conv = torch.nn.Conv2d(in_channels, out_channels, kernel_size, bias=False)
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def pt_conv2d():
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return pt_conv(pt_input)
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pt_mean, pt_std = benchmark_function(pt_conv2d)
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print(f"PyTorch: {pt_mean*1000:.2f} ms ± {pt_std*1000:.2f} ms")
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# TinyTorch
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if TINYTORCH_AVAILABLE:
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try:
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# Use TinyTorch's actual Conv2d implementation
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tt_input = TTTensor(pt_input.numpy())
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tt_conv = TTConv2d(in_channels, out_channels, kernel_size, bias=False)
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# Copy PyTorch weights for fair comparison
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tt_conv.weight.data = pt_conv.weight.detach().numpy()
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def tt_conv2d():
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return tt_conv(tt_input)
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tt_mean, tt_std = benchmark_function(tt_conv2d, warmup=1, runs=3)
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print(f"TinyTorch: {tt_mean:.2f} s ± {tt_std:.2f} s")
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except Exception as e:
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print(f"TinyTorch Conv2d failed: {e}")
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print("Falling back to naive implementation with smaller batch...")
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tt_mean = benchmark_conv2d_naive_small(pt_conv.weight.detach().numpy())
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else:
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# Use smaller batch size for naive implementation (too slow otherwise)
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print("Using smaller batch (8 instead of 128) for naive implementation...")
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tt_mean = benchmark_conv2d_naive_small(pt_conv.weight.detach().numpy())
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ratio = tt_mean / pt_mean
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print(f"Ratio: {ratio:.0f}×")
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return pt_mean * 1000, tt_mean, ratio
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def benchmark_conv2d_naive_small(weight_np):
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"""Benchmark naive conv2d with smaller batch for speed"""
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batch_size_small = 8 # Reduced from 128
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in_channels = 3
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kernel_size = 5
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input_small = np.random.randn(batch_size_small, in_channels, 32, 32)
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def naive_conv2d():
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"""7 nested loops as shown in the paper"""
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B, C_in, H, W = input_small.shape
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C_out, C_in_w, K_h, K_w = weight_np.shape
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H_out = H - K_h + 1
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W_out = W - K_w + 1
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output = np.zeros((B, C_out, H_out, W_out))
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for b in range(B):
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for c_out in range(C_out):
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for h in range(H_out):
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for w in range(W_out):
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for c_in in range(C_in):
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for kh in range(K_h):
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for kw in range(K_w):
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output[b, c_out, h, w] += \
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input_small[b, c_in, h+kh, w+kw] * \
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weight_np[c_out, c_in, kh, kw]
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return output
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tt_mean, tt_std = benchmark_function(naive_conv2d, warmup=0, runs=1)
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print(f"TinyTorch (batch=8): {tt_mean:.2f} s ± {tt_std:.2f} s")
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# Extrapolate to full batch size (linear scaling)
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extrapolated = tt_mean * (128 / 8)
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print(f"TinyTorch (extrapolated to batch=128): {extrapolated:.2f} s")
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return extrapolated
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def benchmark_softmax():
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"""Benchmark softmax on 10K elements"""
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print("\n=== Benchmarking Softmax (10K elements) ===")
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size = 10000
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# PyTorch
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pt_input = torch.randn(size)
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def pt_softmax():
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return torch.nn.functional.softmax(pt_input, dim=0)
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pt_mean, pt_std = benchmark_function(pt_softmax)
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print(f"PyTorch: {pt_mean*1000:.3f} ms ± {pt_std*1000:.3f} ms")
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# TinyTorch - pure Python implementation
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input_np = pt_input.numpy()
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def naive_softmax():
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"""Pure Python softmax"""
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# Subtract max for numerical stability
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x = input_np - np.max(input_np)
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exp_x = np.exp(x)
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return exp_x / np.sum(exp_x)
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tt_mean, tt_std = benchmark_function(naive_softmax, warmup=2, runs=10)
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print(f"TinyTorch: {tt_mean*1000:.3f} ms ± {tt_std*1000:.3f} ms")
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ratio = tt_mean / pt_mean
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print(f"Ratio: {ratio:.0f}×")
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return pt_mean * 1000, tt_mean * 1000, ratio
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def format_time(ms):
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"""Format time in appropriate units"""
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if ms < 1:
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return f"{ms:.2f} ms"
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elif ms < 1000:
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return f"{ms:.1f} ms"
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else:
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return f"{ms/1000:.1f} s"
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def main():
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print("=" * 60)
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print("TinyTorch vs PyTorch Performance Benchmark")
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print("=" * 60)
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print(f"NumPy version: {np.__version__}")
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print(f"PyTorch version: {torch.__version__}")
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print(f"TinyTorch available: {TINYTORCH_AVAILABLE}")
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print("=" * 60)
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results = {}
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# Run benchmarks
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results['matmul'] = benchmark_matmul()
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results['conv2d'] = benchmark_conv2d()
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results['softmax'] = benchmark_softmax()
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# Print LaTeX table
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print("\n" + "=" * 60)
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print("LaTeX Table for paper:")
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print("=" * 60)
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print(r"\begin{table}[t]")
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print(r"\centering")
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print(r"\caption{Runtime comparison: TinyTorch vs PyTorch (CPU).}")
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print(r"\label{tab:performance}")
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print(r"\small")
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print(r"\begin{tabular}{@{}lrrr@{}}")
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print(r"\toprule")
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print(r"Operation & TinyTorch & PyTorch & Ratio \\")
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print(r"\midrule")
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# Format matmul
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pt_mm, tt_mm, ratio_mm = results['matmul']
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print(f"\\texttt{{matmul}} (1K$\\times$1K) & {tt_mm:.0f} ms & {pt_mm:.1f} ms & {ratio_mm:.0f}$\\times$ \\\\")
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# Format conv2d
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pt_conv, tt_conv, ratio_conv = results['conv2d']
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print(f"\\texttt{{conv2d}} (CIFAR batch) & {tt_conv:.1f} s & {pt_conv:.0f} ms & {ratio_conv:.0f}$\\times$ \\\\")
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# Format softmax
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pt_soft, tt_soft, ratio_soft = results['softmax']
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print(f"\\texttt{{softmax}} (10K elem) & {tt_soft:.0f} ms & {pt_soft:.2f} ms & {ratio_soft:.0f}$\\times$ \\\\")
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print(r"\midrule")
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print(r"CIFAR-10 epoch (LeNet) & \textit{TBD} & \textit{TBD} & \textit{TBD} \\")
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print(r"\bottomrule")
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print(r"\end{tabular}")
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print(r"\end{table}")
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print("\n" + "=" * 60)
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print("Summary:")
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print("=" * 60)
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print(f"MatMul (1K×1K): {ratio_mm:6.0f}× slower")
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print(f"Conv2d (CIFAR): {ratio_conv:6.0f}× slower")
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print(f"Softmax (10K): {ratio_soft:6.0f}× slower")
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print(f"Average slowdown: {np.mean([ratio_mm, ratio_conv, ratio_soft]):6.0f}×")
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if __name__ == "__main__":
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main()
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